Conventionally, there is a technology which extracts an object by using background difference.
In the background difference, a difference between a background image prepared beforehand and an observation image (input image) including an object to be extracted is calculated so that an object area can be extracted as a foreground image without needing prior knowledge concerning the object.
However, with a simple calculation of the difference between the static background image and the input image, noise, such as changes in brightness due to changes in weather or indoor lighting, and slight movements of trees or objects other than extraction targets which are included in the background of the input image might be extracted as the foreground image.
Therefore, to flexibly respond to the changes in the background, various background modeling techniques such as a background model estimation technique which uses mixture Gaussian distribution (GMM (Gaussian Mixture Model)) are proposed (for example, see non-patent documents 1 and 2). According to the background model estimation technique using the mixture Gaussian distribution, a robust response to temporary variations in background and rapid variations in background is possible.